[论文解读] Generative AI for learning: Investigating the potential of synthetic learning videos

Daniel Leiker, Ashley A. Ricker+2 more|arXiv (Cornell University)|Apr 7, 2023

Online Learning and Analytics被引用 10

一句话总结

本论文在在线学习中评估AI生成的合成学习视频与传统由教师授课的视频，并未发现学习收益或学习者感知存在显著差异。它表明合成视频可以作为在线教育的传统方法的可行替代品。

ABSTRACT

Recent advances in generative artificial intelligence (AI) have captured worldwide attention. Tools such as Dalle-2 and ChatGPT suggest that tasks previously thought to be beyond the capabilities of AI may now augment the productivity of creative media in various new ways, including through the generation of synthetic video. This research paper explores the utility of using AI-generated synthetic video to create viable educational content for online educational settings. To date, there is limited research investigating the real-world educational value of AI-generated synthetic media. To address this gap, we examined the impact of using AI-generated synthetic video in an online learning platform on both learners content acquisition and learning experience. We took a mixed-method approach, randomly assigning adult learners (n=83) into one of two micro-learning conditions, collecting pre- and post-learning assessments, and surveying participants on their learning experience. The control condition included a traditionally produced instructor video, while the experimental condition included a synthetic video with a realistic AI-generated character. The results show that learners in both conditions demonstrated significant improvement from pre- to post-learning (p<.001), with no significant differences in gains between the two conditions (p=.80). In addition, no differences were observed in how learners perceived the traditional and synthetic videos. These findings suggest that AI-generated synthetic learning videos have the potential to be a viable substitute for videos produced via traditional methods in online educational settings, making high quality educational content more accessible across the globe.

研究动机与目标

• 推动探索AI生成的合成媒体以实现可扩展的在线教育。
• 评估合成视频是否能够在在线环境中匹配传统由教师制作的教学视频。
• 评估对内容获取与学习体验的影响。
• 提供关于高质量教育内容的可获取性和可扩展性方面的证据。

提出的方法

• 将成人学习者（n=83）随机分配到两种微学习条件。
• 进行前测和后测以衡量知识收益。
• 通过调查捕捉学习者的学习体验和感知。
• 比较具有逼真角色的AI生成合成视频与传统教师视频。
• 采用量化与定性结合的混合方法。

实验结果

研究问题

• RQ1 AI生成的合成学习视频在在线学习中是否能带来与传统教师视频相当的内容掌握收益？
• RQ2 学习者在质量和参与度方面如何看待AI生成的视频相对于传统视频？
• RQ3 两种视频模态在学习体验或满意度方面是否存在差异？

主要发现

• 两种条件从前测到后测均显示显著提升（p<.001）。
• AI生成与传统视频在学习收益上无显著差异（p=.80）。
• 在学习者对传统视频与合成视频的感知方面未观察到差异。
• 结果表明AI生成的合成视频可以成为在线教育中传统视频内容的可行替代品。